{"id":2470,"date":"2025-10-19T22:54:57","date_gmt":"2025-10-19T19:54:57","guid":{"rendered":"https:\/\/zekros.com\/?p=2470"},"modified":"2025-10-20T13:26:29","modified_gmt":"2025-10-20T10:26:29","slug":"mlops-yapay-zeka-modellerini-laboratuvardan-gercek-hayata-tasimanin-rotasi","status":"publish","type":"post","link":"https:\/\/zekros.com\/en\/mlops-yapay-zeka-modellerini-laboratuvardan-gercek-hayata-tasimanin-rotasi\/","title":{"rendered":"MLOps: Yapay Zek\u00e2 Modellerini Laboratuvardan Ger\u00e7ek Hayata Ta\u015f\u0131man\u0131n Rotas\u0131"},"content":{"rendered":"
Giri\u015f:<\/p>\n\n\n\n
Yapay zek\u00e2 (YZ) modelleri, art\u0131k birer bilim projesi olmaktan \u00e7\u0131k\u0131p, \u015firketlerin operasyonel omurgas\u0131na yerle\u015fen, verimlili\u011fi ve karl\u0131l\u0131\u011f\u0131 do\u011frudan etkileyen kritik varl\u0131klara d\u00f6n\u00fc\u015ft\u00fc. Ancak, bir derin \u00f6\u011frenme modelinin kodlan\u0131p e\u011fitilmesi, hikayenin yaln\u0131zca ba\u015flang\u0131c\u0131d\u0131r. Ger\u00e7ek meydan okuma, bu modeli \u00fcretim ortam\u0131na ta\u015f\u0131mak, performans\u0131n\u0131 s\u00fcrekli izlemek ve de\u011fi\u015fen ko\u015fullara adapte etmektir. \u0130\u015fte bu karma\u015f\u0131k s\u00fcreci y\u00f6netmek i\u00e7in modern m\u00fchendisli\u011fin alt\u0131n kural\u0131 olan MLOps (Makine \u00d6\u011frenimi Operasyonlar\u0131) devreye girer.<\/p>\n\n\n\n
Zekros olarak, bizler yaln\u0131zca \u00fcst\u00fcn YZ modelleri geli\u015ftirmekle kalm\u0131yor, ayn\u0131 zamanda bu modellerin ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fc ba\u015ftan sona y\u00f6neterek \u015firketiniz i\u00e7in s\u00fcrd\u00fcr\u00fclebilir bir de\u011fer yarat\u0131yoruz. Bu yaz\u0131da, bir YZ \u00e7\u00f6z\u00fcm\u00fcn\u00fcn operasyonel m\u00fckemmelli\u011fe ula\u015fmas\u0131n\u0131 sa\u011flayan MLOps’un \u00fc\u00e7 temel a\u015famas\u0131n\u0131 inceleyece\u011fiz: Model Geli\u015ftirme, Da\u011f\u0131t\u0131m (Deployment) ve \u0130zleme.<\/p>\n\n\n\n
1. Model Geli\u015ftirme: Fikri \u00dcr\u00fcne D\u00f6n\u00fc\u015ft\u00fcrmek<\/p>\n\n\n
\n<\/figure>\n<\/div>\n\n\n
Her ba\u015far\u0131l\u0131 end\u00fcstriyel YZ \u00e7\u00f6z\u00fcm\u00fc, do\u011fru verinin toplanmas\u0131 ve do\u011fru metodolojinin belirlenmesiyle ba\u015flar. Bu a\u015famada, Zekros ekibi, m\u00fc\u015fterilerimizin operasyonel verilerinden yola \u00e7\u0131karak end\u00fcstriyel problemleri \u00e7\u00f6zen modeller in\u015fa eder.<\/p>\n\n\n\n
Zekros \u00d6rne\u011fi: Bir otomotiv par\u00e7a \u00fcreticisi i\u00e7in geli\u015ftirdi\u011fimiz otomatik kalite kontrol \u00e7\u00f6z\u00fcm\u00fc bu s\u00fcrecin m\u00fckemmel bir \u00f6rne\u011fidir. \u00dcretim hatt\u0131ndan anl\u0131k olarak gelen y\u00fcksek \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc g\u00f6r\u00fcnt\u00fcleri kullanarak, Evri\u015fimli Sinir A\u011flar\u0131 (CNN) ile en ufak y\u00fczey kusurlar\u0131n\u0131 dahi tespit eden bir model geli\u015ftirdik. Geli\u015ftirme a\u015famas\u0131nda, farkl\u0131 \u0131\u015f\u0131kland\u0131rma ko\u015fullar\u0131 ve \u00fcr\u00fcn varyasyonlar\u0131na g\u00f6re modelin genelleme yetene\u011fi kapsaml\u0131 bir \u015fekilde test edildi. Bu sayede, model sadece laboratuvar ortam\u0131nda de\u011fil, sahan\u0131n dinamik ve zorlu \u015fartlar\u0131nda da en y\u00fcksek performans\u0131 g\u00f6sterecek \u015fekilde haz\u0131rland\u0131.<\/p>\n\n\n\n
2. Model Da\u011f\u0131t\u0131m\u0131 (Deployment): Modeli Ger\u00e7ek Operasyonlara Entegre Etmek<\/p>\n\n\n
\n<\/figure>\n<\/div>\n\n\n
Bir modelin da\u011f\u0131t\u0131m\u0131, onun sadece bir kod blo\u011fu olmaktan \u00e7\u0131k\u0131p, operasyonel s\u00fcre\u00e7lerin ayr\u0131lmaz bir par\u00e7as\u0131 haline geldi\u011fi and\u0131r. Zekros, bu ge\u00e7i\u015fi manuel ad\u0131mlardan ar\u0131nd\u0131r\u0131p, otomatikle\u015ftirilmi\u015f bir i\u015f ak\u0131\u015f\u0131<\/strong> (CI\/CD) arac\u0131l\u0131\u011f\u0131yla y\u00f6netir. Bu yakla\u015f\u0131m, modelin h\u0131zl\u0131, g\u00fcvenilir ve \u00f6l\u00e7eklenebilir bir \u015fekilde \u00fcretim ortam\u0131na entegre edilmesini sa\u011flar.<\/p>\n\n\n\n
Zekros \u00d6rne\u011fi: Bir petrokimya tesisinde kullan\u0131lan kestirimci bak\u0131m \u00e7\u00f6z\u00fcm\u00fc i\u00e7in geli\u015ftirdi\u011fimiz Tekrarlayan Sinir A\u011f\u0131 (RNN) modelini ele alal\u0131m. Bu model, kritik ekipmanlardan gelen sens\u00f6r verilerini (s\u0131cakl\u0131k, bas\u0131n\u00e7, titre\u015fim vb.) s\u00fcrekli analiz ederek potansiyel ar\u0131zalar\u0131 \u00f6nceden tahmin ediyor. Model, tesisteki mevcut SCADA sistemine, bir API servisi olarak da\u011f\u0131t\u0131ld\u0131. MLOps boru hatt\u0131m\u0131z sayesinde, yeni bir versiyonu gerekti\u011finde, \u00fcretim s\u00fcrecini aksatmadan dakikalar i\u00e7inde g\u00fcncelleyebiliyoruz.<\/p>\n\n\n\n
3. Model \u0130zleme ve Bak\u0131m: De\u011fi\u015fen D\u00fcnyada S\u00fcrekli Uyum Sa\u011flamak<\/p>\n\n\n
\n<\/figure>\n<\/div>\n\n\n
Bir modelin \u00fcretimde \u00e7al\u0131\u015fmaya ba\u015flamas\u0131, hikayenin sonu de\u011fil, en kritik b\u00f6l\u00fcm\u00fcd\u00fcr. Ger\u00e7ek d\u00fcnya verileri, modelin e\u011fitildi\u011fi verilerden farkl\u0131 olabilir (veri kaymas\u0131 – data drift). Bu durum, modelin tahmin performans\u0131n\u0131n zamanla d\u00fc\u015fmesine neden olur.<\/p>\n\n\n\n
MLOps’un izleme a\u015famas\u0131, bu d\u00fc\u015f\u00fc\u015f\u00fc erkenden tespit etmek i\u00e7in bir erken uyar\u0131 sistemi g\u00f6revi g\u00f6r\u00fcr.<\/p>\n\n\n\n
Zekros \u00d6rne\u011fi: Geli\u015ftirdi\u011fimiz ak\u0131ll\u0131 dok\u00fcman y\u00f6netim sisteminde kullan\u0131lan Transformer mimarisi, m\u00fchendislik prosed\u00fcrlerinden olu\u015fan binlerce sayfay\u0131 analiz ediyor. \u0130zleme sistemimiz, modelin do\u011fal dildeki sorulara verdi\u011fi yan\u0131tlar\u0131n do\u011frulu\u011funu s\u00fcrekli takip ediyor. E\u011fer bir veri kaymas\u0131 (\u00f6rne\u011fin, yeni ekipman tipleri veya prosed\u00fcrlerdeki de\u011fi\u015fiklikler) tespit edilirse, sistem otomatik olarak uyar\u0131 vererek modelin en yeni verilerle yeniden e\u011fitilmesini \u00f6neriyor. Bu sayede, modelimizin her zaman g\u00fcncel ve do\u011fru bilgi sunmas\u0131n\u0131 garanti ediyoruz.<\/p>\n\n\n\n
Sonu\u00e7: Zekros ile Kesintisiz Operasyonel M\u00fckemmellik<\/p>\n\n\n\n
MLOps, sadece teknolojik bir s\u00fcre\u00e7 y\u00f6netimi de\u011fil, ayn\u0131 zamanda yapay zek\u00e2 yat\u0131r\u0131mlar\u0131n\u0131z\u0131n geri d\u00f6n\u00fc\u015f\u00fcn\u00fc g\u00fcvence alt\u0131na alan stratejik bir yakla\u015f\u0131md\u0131r. Geli\u015ftirdi\u011finiz model ne kadar ileri seviye olursa olsun, do\u011fru MLOps s\u00fcre\u00e7leri olmadan end\u00fcstriyel bir ortamda s\u00fcrd\u00fcr\u00fclebilir bir ba\u015far\u0131 elde etmesi m\u00fcmk\u00fcn de\u011fildir.<\/p>\n\n\n\n
Zekros olarak, modellerinizin ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fc ba\u015ftan sona y\u00f6neterek operasyonel m\u00fckemmelli\u011fe ula\u015fman\u0131z\u0131 sa\u011fl\u0131yoruz. Birlikte, yapay zek\u00e2n\u0131n g\u00fcc\u00fcn\u00fc \u00fcretim hatt\u0131n\u0131za ta\u015f\u0131yal\u0131m ve i\u015f s\u00fcre\u00e7lerinizde devrim yaratacak \u00e7\u00f6z\u00fcmler geli\u015ftirelim.<\/p>","protected":false},"excerpt":{"rendered":"
Giri\u015f: Yapay zek\u00e2 (YZ) modelleri, art\u0131k birer bilim projesi olmaktan \u00e7\u0131k\u0131p, \u015firketlerin operasyonel omurgas\u0131na yerle\u015fen, verimlili\u011fi ve karl\u0131l\u0131\u011f\u0131 do\u011frudan etkileyen kritik varl\u0131klara d\u00f6n\u00fc\u015ft\u00fc. Ancak, bir derin \u00f6\u011frenme modelinin kodlan\u0131p e\u011fitilmesi, hikayenin yaln\u0131zca ba\u015flang\u0131c\u0131d\u0131r. Ger\u00e7ek meydan okuma, bu modeli \u00fcretim ortam\u0131na ta\u015f\u0131mak, performans\u0131n\u0131 s\u00fcrekli izlemek ve de\u011fi\u015fen ko\u015fullara adapte etmektir. \u0130\u015fte bu karma\u015f\u0131k s\u00fcreci y\u00f6netmek i\u00e7in modern m\u00fchendisli\u011fin […]<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[18,86],"tags":[224,220,222,217,216,215,223,221,225,219,119,218,117,226],"class_list":["post-2470","post","type-post","status-publish","format-standard","hentry","category-all-topics","category-yz","tag-cicd-2","tag-computervision-2","tag-datadrift-2","tag-iiot-2","tag-industrialai-2","tag-mlops-2","tag-modelmonitoring-2","tag-nlp-2","tag-operationalexcellence-2","tag-predictivemaintenance-2","tag-scada","tag-smartmanufacturing-2","tag-yapayzeka","tag-zekros-2"],"yoast_head":"\n
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